Search Results (217)

Urban streets serve as essential spaces for commercial activities and social interaction, yet the mechanisms through which their landscape elements influence consumption vitality remain insufficiently explored. Focusing on Lixia District, Jinan, China, this study integrates street-view image semantic segmentation with machine learning techniques to capture the nonlinear interactions between streetscape features and consumption vitality, thereby establishing an analytical framework for examining their associations. The results show that: (1) pedestrian-friendly facilities are significantly associated with higher street-level consumption vitality, with benches and streetlights showing marked effects once their visual proportions exceed 10% and 12%, respectively; (2) the visual proportion of parking space becomes positively associated with consumption vitality when exceeding 0.15, whereas carriageway proportion shows an overall negative association; (3) the marginal effect of advertising density gradually diminishes, with billboard visibility ratios above 25% exhibiting saturated impacts; (4) when green-view visibility exceeds 30%, consumption vitality increases substantially, peaking within the 35–40% range; (5) potential synergies or trade-offs exist among streetscape elements—compared with individual factors, the combinations of benches and parking spaces, benches and billboards, as well as parking spaces and billboards, are associated with higher street-level consumption vitality. In contrast, combinations involving a larger sky view ratio are often linked to lower consumption vitality, suggesting that overly open spaces may weaken consumer attractiveness. This study not only extends the methodological toolkit for analyzing consumption vitality but also provides theoretical and practical guidance for the refined design and experiential construction of urban street environments. Full article

The MoonRay project is carrying out a concept study of a permanent lunar cosmic-ray (CR) and gamma-ray observatory, in view of the implementation of habitats on our satellite. The idea is to build a modular telescope that will be able to overcome the limitations, in available power and weight, of the present generation of CR instruments in Low Earth Orbit, while carrying out high-energy gamma-ray observations from a vantage point at the South Pole of the Moon. An array of fully independent modules (towers), with limited individual size and mass, can provide an acceptance more than one order of magnitude larger than instruments in flight at present. The modular telescope is designed to be deployed progressively, during a series of lunar missions, while collecting meaningful scientific data at the intermediate stages of its implementation. The operational power will be made available by the facilities maintaining the lunar habitats. With a geometric factor close to 15 m²sr and about 8 times larger sensitive area than FERMI-LAT, MoonRay will be able to carry out a very rich observational program over a time span of a few decades with an energy reach of 10 PeV allowing the exploration of the CR “knee” and the observation of the Southern Sky with gamma rays well into the TeV scale. Each tower (of approximate size 20 cm × 20 cm ×100 cm) is equipped with three instruments. A combined Charge and Time-of-Flight detector (CD-ToF) can identify individual cosmic elements, leveraging on an innovative two-layered array of pixelated Low-Gain Avalanche Diode (LGAD) sensors, with sub-ns time resolution. The latter can achieve an unprecedented rejection power against backscattered radiation from the calorimeter. It is followed by a tracker, providing also photon conversion, and by a thick crystal calorimeter (55 radiation lengths, 3 proton interaction lengths at normal incidence) with an energy resolution of 30–40% (1–2%) for protons (electrons) and a proton/electron rejection in excess of 10⁵. A time resolution close to 100 ps has been obtained, with prototypal arrays of 3 mm × 3 mm LGAD pixels, in a recent test campaign carried out at CERN with Pb beam fragments. Full article

With rapid urbanization and intensifying climate change impacts, the thermal comfort performance of semi-outdoor spaces has emerged as a critical issue in sustainable urban design and housing development. However, the unique void decks of residential environments remain underexplored in the existing literature. This study addresses the knowledge gap by investigating how morphological characteristics influence microclimatic conditions and user satisfaction in high-density subtropical residential environments. Field measurements and questionnaire surveys were conducted across 18 void decks in four representative Shenzhen communities during summer 2024, examining air temperature, relative humidity, wind velocity, mean radiant temperature, and UTCI alongside users’ thermal perceptions. Hierarchical cluster analysis identified three distinct typologies based on spatial attributes: North–South-Ventilated (NS-VD), Single-Directional (SD-VD), and Oblique-Oriented (OO-VD). Ridge regression analysis revealed seven critical configuration variables—height-to-depth ratio, orientation, angle with wind, number of open sides, sky view factor, green view factor, and height from ground—collectively explaining 51.2% of UTCI variation. The results were as follows: (1) we identified morphological typologies and quantify microclimate variations across spatial configurations; (2) established quantitative relationships between objective thermal metrics and subjective thermal perceptions; and (3) developed evidence-based design recommendations for enhancing thermal environments in subtropical residential contexts. The findings support climate-responsive design for high-density residential environments by providing a scientific basis for optimizing microclimates and enhancing community vitality. Full article

Accurately mapping urban residents’ exposure to green space at high spatiotemporal resolutions is essential for assessing disparities and equality across blocks and enhancing urban environment planning. In this study, we developed a framework to generate hourly green space exposure maps at 0.5 m resolution using multiple sources of remote sensing data and an Object-Based Image Classification with Graph Convolutional Network (OBIC-GCN) model. Taking the main urban area in Nanjing city of China as the study area, we proposed a Dynamic Residential Green Space Exposure (DRGE) metric to reveal disparities in green space access across four housing price blocks. The Palma ratio was employed to explain the inequity characteristics of DRGE, while XGBoost (eXtreme Gradient Boosting) and SHAP (SHapley Additive explanation) methods were utilized to explore the impacts of built environment factors on DRGE. We found that the difference in daytime and nighttime DRGE values was significant, with the DRGE value being higher after 6:00 compared to the night. Mean DRGE on weekends was about 1.5 times higher than on workdays, and the DRGE in high-priced blocks was about twice that in low-priced blocks. More than 68% of residents in high-priced blocks experienced over 8 h of green space exposure during weekend nighttime (especially around 19:00), which was much higher than low-price blocks. Moreover, spatial inequality in residents’ green space exposure was more pronounced on weekends than on workdays, with lower-priced blocks exhibiting greater inequality (Palma ratio: 0.445 vs. 0.385). Furthermore, green space morphology, quantity, and population density were identified as the critical factors affecting DRGE. The optimal threshold for Percent of Landscape (PLAND) was 25–70%, while building density, height, and Sky View Factor (SVF) were negatively correlated with DRGE. These findings address current research gaps by considering population mobility, capturing green space supply and demand inequities, and providing scientific decision-making support for future urban green space equality and planning. Full article

PM_2.5 pollution is a significant environmental problem in global urbanization. However, traditional macro-scale studies are constrained by data resolution limitations, failing to accurately characterize the microscale built environment or thoroughly investigate its spatially heterogeneous effects on PM_2.5 concentrations. To address this gap, this study constructs a multidisciplinary framework of “Street View Imagery element extraction–spatial heterogeneity modeling–planning strategy optimization” with Xi’an as the case. Leveraging machine learning techniques, the study employs the ResNet50 deep learning model and the ADE20K dataset to precisely extract ten microscale built environment factors from tens of thousands of street view images. Combined with the High-resolution and High-quality Ground-level PM_2.5 Dataset for China, Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Multiscale Geographically Weighted Regression (MGWR) models were used to systematically reveal the impacts of the microscale built environment on PM_2.5 concentrations. Ten built environment factors were identified with varying spatial heterogeneity in their effects on the PM_2.5 concentrations, as follows: (1) factors with positive effects, in descending order of strength, include building, wall, fence, tree, sky, and grass; (2) factors with negative effects, in descending order of strength, include sidewalk, plant, and car; (3) compared with other factors, the road factor showed a relatively weaker effect. This research provides decision-making support for targeted urban planning and environmental protection, while offering valuable references for air pollution control in other cities. Full article

Cold-air pooling (CAP) and frost risk represent significant climate-related hazards in karstic and agricultural environments, where local topography and surface cover strongly modulate microclimatic conditions. This study focuses on the Mohos sinkhole, Hungary’s cold pole, situated on the Bükk Plateau, to investigate the formation, structure, and persistence of CAPs in a Central European karst depression. High-resolution terrain-based modeling was conducted using UAV-derived digital surface models combined with multiple GIS tools (Sky-View Factor, Wind Exposition Index, Cold Air Flow, and Diurnal Anisotropic Heat). These models were validated and enriched by multi-level temperature measurements and thermal imaging under various synoptic conditions. Results reveal that temperature inversions frequently form during clear, calm nights, leading to extreme near-surface cold accumulation within the sinkhole. Inversions may persist into the day due to topographic shading and density stratification. Vegetation and basin geometry influence radiative and turbulent fluxes, shaping the spatial extent and intensity of cold-air layers. The CAP is interpreted as part of a broader interconnected multi-sinkhole system. This integrated approach offers a transferable, cost-effective framework for terrain-driven frost hazard assessment, with direct relevance to precision agriculture, mesoscale model refinement, and site-specific climate adaptation in mountainous or frost-sensitive regions. Full article

As a key strategy for broader sustainability, effective street regeneration requires a precise understanding of the built environment’s influence mechanisms. However, existing approaches often overlook the functional heterogeneity of streets and the non-linearity of their influence mechanisms. Addressing this gap, we developed an approach to analyze these mechanisms of the built environment, differentiated by street function. Integrating multi-source urban data, street quality was measured across three dimensions (visual quality, vibrancy, and functionality), and specialized weights for streets were determined according to their dominant functions. Applying this approach in Shanghai, we explained the non-linear effects of the built environment for each street function type through separate GBDT models and SHAP analysis. The results reveal that the influence mechanisms of built environment factors vary significantly across dominant street functions. Specifically, the heterogeneity of critical activation thresholds and saturation points provides direct evidence for more targeted regeneration strategies. Key findings highlight that a strong sense of enclosure is a priority for the quality of residential street, as measured by a low Sky View Factor. In contrast, vertical development intensity is a priority for commercial streets, as Floor Area Ratio requires a high activation threshold to exert a positive influence. In short, this research provides a computational approach that enables precise and data-driven interventions, which contribute to sustainable urban development. Full article

The urban thermal environment poses a significant challenge to public health and sustainable urban development. Conventional pre-defined classification schemes, such as the Local Climate Zone (LCZ) system, often fail to capture the highly heterogeneous structure of complex urban areas, thus limiting their applicability. This study introduces a novel framework for urban thermal environment analysis, leveraging multi-source data and eXplainable Artificial Intelligence to investigate the driving mechanisms of Land Surface Temperature (LST) across various urban form types. Focusing on the area within Beijing’s 5th Ring Road, this study employs a K-Means clustering algorithm to classify urban blocks into nine distinct types based on their building morphology. Subsequently, an eXtreme Gradient Boosting (XGBoost) model, coupled with the SHapley Additive exPlanations (SHAP) method, is utilized to analyze the non-linear impacts of ten selected driving factors on LST. The findings reveal that: (1) The Compact Mid-rise type exhibits the highest annual average LST at 296.59 K, with a substantial difference of 11.29 K observed between the hottest and coldest block types. (2) SHAP analysis identifies the Normalized Difference Built-up Index (NDBI) as the most significant warming factor across all types, while the Sky View Factor (SVF) plays a crucial cooling role in high-rise areas. Conversely, road density (RD) shows a negative correlation with LST in Open Low-rise areas. (3) The influence of urban form is twofold: increased building height (BH) can induce warming by trapping heat while simultaneously providing a cooling effect through shading. (4) The impact of land use functional zones on LST is significantly modulated by urban form, with temperature differences of up to 2 K observed between different functional zones within compact block types. The analytical framework proposed herein holds significant theoretical and practical implications for achieving fine-grained thermal environment governance and fostering sustainable development in the context of global urbanization. Full article

Comprehensive evaluation of suburban landscape perception is essential for improving environmental quality and fostering integrated urban–rural development. Despite its importance, limited research has systematically extracted local visual features and analyzed influencing factors in suburban landscapes using multi-source data and machine learning. This study investigated Chongming District, a suburban area of Shanghai. Using Baidu Street View 360° panoramic images, local visual features were extracted through semantic segmentation of street view imagery, spatial multi-clustering, and random forest classification. A geographic detector model was employed to explore the relationships between landscape characteristics and their driving factors. The findings of the study indicate (1) significant spatial variations in the green visibility, sky openness, building density, road width, facility diversity, and enclosure integrity; (2) an intertwined spatial pattern of blue, green, and gray spaces; (3) the emergence of natural environment dimension factors as the primary drivers influencing the spatial configuration. In the suburban industrial dimension, the interaction between the GDP and commercial vitality exhibits the highest level of synergy. Based on these findings, targeted strategies are proposed to enhance the distinctive landscape features of Chongming Island. This research framework and methodology are specifically applied to Chongming District as a case study. Future studies should consider modifying the algorithms and index systems to better reflect other study areas, thereby ensuring the validity and precision of the results. Full article

Understanding the seasonal nonlinear relationship between urban heat island (UHI) and multidimensional urban morphological patterns is crucial for regulating the urban thermal environment. To address this, this study quantified the contributions and sensitivities of urban morphology to land surface temperature (LST) variations and revealed their influencing pathways across four seasons in Beijing, using automated machine learning, SHapley Additive exPlanations interpretation, partial dependence analysis, and structural equation modeling. The results showed significant seasonal variations at the grid scale of 200 m. It was revealed that Normalized Difference Vegetation Index (NDVI) emerged as the most significant indicator affecting LST, followed by building height (BH) and building coverage ratio (BCR), while sky view factor and frontal area index had the least impact. BH was more influential than NDVI, affecting LST during winter. Additionally, sensitivity analysis revealed that impervious surface area, BCR, and mean building volume had positive relationships with LST. In contrast, NDVI and BH negatively affected LST with a noticeable cooling effect, particularly in summer. Furthermore, the total effects of all indicators on LST were negative, with the greatest in spring and the least in winter. Three-dimensional indicators generally exhibited more pronounced direct and total effects than two-dimensional indicators, except in winter. These findings can offer valuable insights for regulating seasonal surface UHI to maximize thermal environmental benefits. Full article

Urban form plays a crucial role in regulating urban thermal environments and air pollution patterns. However, the indirect mechanisms through which urban form influences PM2.5 concentrations via land surface temperature (LST) remain poorly understood. This study investigates these pathways by analyzing representative two- and three-dimensional urban form indices (UFIs) in the central urban area of Taiyuan, China. Multiple log-linear regression and mediation analysis were applied to evaluate the combined effects of urban form on LST and PM2.5. The results indicate that UFIs significantly influence both LST and PM2.5. The frontal area index (FAI) and sky view factor (SVF) emerged as key variables, with LST playing a significant mediating role. The indirect pathways affecting PM2.5 via LST, along with the direct LST-PM2.5 correlation, exhibit pronounced seasonal differences in direction and intensity. Moreover, different urban functional zones exhibit heterogeneous responses to the same form indices, highlighting the spatial variability of these linkages. These findings underscore the importance of incorporating seasonal and spatial differences into urban design. Accordingly, this study proposes targeted urban form optimization strategies to improve air quality and thermal comfort, offering theoretical insights and practical guidance for sustainable urban planning. Full article

Urban outdoor thermal comfort (OTC) has become an increasingly critical issue under the pressures of urbanization and climate change. Comparative analyses of urban blocks with distinct spatial morphologies are essential for identifying OTC issues and proposing targeted optimization strategies. However, existing studies predominantly rely on microclimate numerical simulations, while comparative assessments of OTC from the human thermal perception perspective remain limited. This study employs the thermal walk method, integrating microclimatic measurements with thermal perception questionnaires, to conduct on-site OTC investigations across three urban blocks with contrasting spatial morphologies—a business district (BD), a residential area (RA), and a historical neighborhood (HN)—in Beijing, a hot summer and cold winter climate city. The results reveal substantial OTC differences among the blocks. However, these differences demonstrated great seasonal and temporal variations. In summer, BD exhibited the best OTC (mTSV = 1.21), while HN performed the worst (mTSV = 1.72). In contrast, BD showed the poorest OTC in winter (mTSV = −1.57), significantly lower than HN (−1.11) and RA (−1.05). This discrepancy was caused by the unique morphology of different blocks. The sky view factor emerged as a more influential factor affecting OTC over building coverage ratio and building height, particularly in RA (r = 0.689, p < 0.01), but its impact varied by block, season, and sunlight conditions. North–South streets generally perform better OTC than East–West streets, being 0.26 units cooler in summer and 0.20 units warmer in winter on the TSV scale. The study highlights the importance of incorporating more applicable physical parameters to optimize OTC in complex urban contexts and offering theoretical support for designing climate adaptive urban spaces. Full article

In response to the inability of traditional remote sensing technology to accurately capture the micro-topographic features of landslide surfaces in vegetated areas under complex terrain conditions, this paper proposes a method for enhanced landslide terrain display and trace recognition based on airborne LiDAR technology. Firstly, a high-precision LiDAR-DEM is constructed using preprocessed LiDAR point cloud data, and visual images are generated using visualization methods, including hillshade, slope, openness, and Sky View Factor (SVF). Secondly, pixel-level image fusion methods are applied to the visual images to obtain enhanced display images of the landslide terrain. Finally, a threshold is determined through a fractal model, and the Mean-Shift algorithm is utilized for clustering and denoising to extract landslide traces. The results indicate that employing pixel-level image fusion technology, which combines the advantageous features of multiple terrain visualization images, effectively enhances the display of landslide micro-topography. Moreover, based on the enhanced display images, the fractal model and the Mean-Shift algorithm are applied for denoising to extract landslide traces. Compared to orthophotos, this method can effectively and accurately extract landslide traces. The findings of this study provide valuable references for the enhanced display and trace recognition of landslide terrain in densely vegetated areas within complex mountainous areas, thereby providing technical support for emergency investigations of landslide disasters. Full article

This study aims to (1) analyze thermal comfort at outdoor sport events held outside of fixed venues or locations; (2) establish a method for evaluating environmental thermal comfort for large-scale, long-term outdoor activities; and (3) provide suggestions for the arrangement of shifts in routes and participants for heat warning and mitigation. Taiwan ReAnalysis Downscaling (TReAD) data, Sky View Factors (SVFs), GSV2SVF tool, and RayMan Pro were applied to analyze and evaluate thermal comfort at the 2021 Torch Relay Round the Island, Taiwan. In this study, modified Physiologically Equivalent Temperature (mPET), Wet Bulb Globe Temperature (WBGT), and Universal Thermal Climate Index (UTCI) were estimated and selected as thermal indicators for the purpose of obtaining a more comprehensive perspective. We also define and present thermal performance with a simple traffic light symbol (green: comfortable/yellow: warm/red: hot) and try to go beyond the concept of heat and visualize it in an easy-to-understand way. Full article

This study explores the impact of microclimatic variations on thermal perception and walking experience in Béjaïa, Algeria, focusing on two contrasting urban areas: the compact historic medina and the modern lower city. A mixed-method approach combined microclimatic measurements (Ta, Ts, Va, RH) with subjective evaluations from 70 participants. After urban morphological analysis, walking itineraries were designed and studied through accompanied walks. Participants reported their thermal sensations and walking comfort via questionnaires and mental maps, while environmental data were simultaneously collected (21–28 July 2022). Results show that transitions between urban fabrics significantly affect thermal sensation and walking thermal comfort (WTC). Strong correlations were observed between surface temperature (Ts) and sky view factor (SVF), and between ASV and WTC (Kendall’s τᵦ = 0.79, 95% CI [0.70, 0.88]). Beyond physical factors, perceptual variables like vegetation (OR = 1.50), maintenance (OR = 1.40), and views (OR = 1.30) significantly increased WTC, while fatigue (OR = 0.70) and safety concerns (OR = 0.80) reduced it. The findings highlight strong contrasts between the two areas and support planning strategies emphasizing vegetation, spatial optimization, and the integration of perceptual thermal factors in urban design. Full article